Sequential Estimation
Wiley Series in Probability and Statistics
1. Auflage März 1997
504 Seiten, Hardcover
Wiley & Sons Ltd
The only comprehensive guide to the theory and practice of one oftoday's most important probabilistic techniques
The past 15 years have witnessed many significant advances insequential estimation, especially in the areas of three-stage andnonparametric methodology. Yet, until now, there were no referencesdevoted exclusively to this rapidly growing statisticalfield.
Sequential Estimation is the first, single-source guide to thetheory and practice of both classical and modern sequentialestimation techniques--including parametric and nonparametricmethods. Researchers in sequential analysis will appreciate theunified, logically integrated treatment of the subject, as well ascoverage of important contemporary procedures not covered in moregeneral sequential analysis texts, such as:
* Shrinkage estimation
* Empirical and hierarchical Bayes procedures
* Multistage sampling and accelerated sampling procedures
* Time-sequential estimation
* Sequential estimation in finite population sampling
* Reliability estimation and capture-recapture methodologiesleading to sequential tagging schemes
An indispensable resource for researchers in sequential analysis,Sequential Estimation is an ideal graduate-level text as well.
Probabilistic Results in Sequential Analysis.
Some Basic Concepts for Fixed-Sample Estimation.
General Aspects of Sequential Estimation.
Sequential Bayesian Estimation.
Multistage Estimation.
Parametric Sequential Point Estimation.
Parametric Sequential Confidence Estimation.
Nonparametric Sequential Point Estimation.
Nonparametric Sequential Confidence Estimation.
Estimation Following Sequential Tests.
Time-Sequential Estimation Problems.
Sequential Estimation in Reliability Models.
Sequential Estimation of the Size of a Finite Population.
Stochastic Approximation.
References.
Indexes.
"...sequential estimation...is the focus...including bothparametric and nonparametric methods..." (Quarterly of AppliedMathematics, Vol. LIX, No. 3, September 2001)
"an excellent source for all researchers in the field" (Statistics& Decisions, Vol.20, No.1, 2002)
NITIS MUKHOPADHYAY, PhD, is a professor in the University ofConnecticut Department of Statistics.
PRANAB K. SEN, PhD, is a professor in the Department ofBiostatistics and Statistics of the University of North Carolina.
